Small area prediction for a unit-level lognormal model

@article{Berg2014SmallAP,
  title={Small area prediction for a unit-level lognormal model},
  author={Emily Berg and Hukum Chandra},
  journal={Comput. Stat. Data Anal.},
  year={2014},
  volume={78},
  pages={159-175}
}
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Topics on small area estimation, multilevel models, and semiparametric imputation
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County level estimates of mean sheet and rill erosion from the Conservation Effects Assessment Project (CEAP) survey are useful for program development and evaluation. As a result of small county…
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References

SHOWING 1-10 OF 26 REFERENCES
An Empirical Best Linear Unbiased Prediction Approach to Small-Area Estimation of Crop Parameters
Accurate county (small-area) level estimation of crop and livestock items is an important priority for the USDA's National Agricultural Statistics Service (NASS). We consider an empirical best linear…
Resampling-based empirical prediction: an application to small area estimation
Best linear unbiased prediction is well known for its wide range of applications including small area estimation. While the theory is well established for mixed linear models and under normality of…
Robust small area estimation
Small area estimation has received considerable attention in recent years because of growing demand for small area statistics. Basic area‐level and unit‐level models have been studied in the…
M-quantile models for small area estimation
Small area estimation techniques typically rely on regression models that use both covariates and random effects to explain variation between the areas. However, such models also depend on strong…
The estimation of the mean squared error of small-area estimators
Abstract Small-area estimation has received considerable attention in recent years because of a growing demand for reliable small-area statistics. The direct-survey estimators, based only on the data…
The Mean Squared Error of Small Area Predictors Constructed With Estimated Area Variances
In the small area estimation literature, the sampling error variances are customarily assumed to be known or to depend on a finite number of parameters. We consider the empirical best linear unbiased…
Bayesian transformed models for small area estimation
TLDR
H hierarchical models with power transformations are proposed for improving the precision of small area predictions in sample surveys to estimate mean acreage under a specified crop for counties in Iowa.
Small-area estimation based on natural exponential family quadratic variance function models and survey weights
We propose pseudo empirical best linear unbiased estimators of small-area means based on natural exponential family quadratic variance function models when the basic data consist of survey-weighted…
Best Predictive Small Area Estimation
We derive the best predictive estimator (BPE) of the fixed parameters under two well-known small area models, the Fay–Herriot model and the nested-error regression model. This leads to a new…
An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data
Abstract Knowledge of the area under different crops is important to the U.S. Department of Agriculture. Sample surveys have been designed to estimate crop areas for large regions, such as…
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